Hearing aids and methods and apparatus for audio fitting thereof

ABSTRACT

A field ready, unsupervised-use ready, method and apparatus for audio fitting a hearing aid is described in a hand held configuration having paired comparisons (hearing selections) stored in and derivable from a memory therein. The paired comparisons are presented one at a time to a user and a preferred selection for each paired comparison is made by a select indicator after the user toggles back and forth between the selections for as many times necessary in determining their preferences. A genetic algorithm converges all the preferences upon a single solution. Crossover and mutation genetic algorithm operators operate on a linear range of indexes representative of parametric values of the pairs. A fully integrated hearing aid having all the above described features incorporated therein is also presented.

FIELD OF THE INVENTION

The present invention relates to hearing aids. In particular, it relatesto methods and apparatus for efficaciously audio fitting them. Morebroadly, however, the present invention relates to perceptually tuningany system, such as hearing aids. Even more broadly, the presentinvention relates to genetic algorithms utilizing user input selectionfrom paired comparisons for performing the tuning. Still even morebroadly, the present invention relates to genetic algorithm crossoverand mutation operators for use in a genetic algorithm that neitheroperates directly on a parametric value nor a string of bitsrepresenting the parametric value.

BACKGROUND OF THE INVENTION

Many fields encounter problems associated with perceptually tuning asystem. For example, in perceptually tuning or “fitting” a hearing aid,antiquated methods subjected a single sensorineurally impaired user tomany and various audio-related settings of their hearing aid and, oftenvia technical support from an audiologist, individually determined thepreferred settings for that single user. This approach, however, hasproven itself lacking in universal applicability.

Thus, prescriptive fitting formulas have evolved whereby large numbersof users can become satisfactorily fit by adjusting the same hearing aiddevice. With the advent of programmable hearing aids, this approach hasbecome especially more viable. This approach is, however, still toogeneral because individual preferences are often ignored. Therecurrently exists no accepted selection strategy that provides astructured and efficient approach to incorporating individualpreferences into hearing aid fittings.

In one particular hearing aid fitting selection strategy, pairedcomparisons were used. In this strategy, users were presented with achoice between two actual hearing aids from a large set of hearing aidsand asked to compare them in an iterative round robin, doubleelimination tournament or modified simplex procedure until one hearingaid “winner” having optimum frequency-gain characteristics was convergedupon. These uses of paired comparisons, however, are extremelyimpractical in time and financial resources. Moreover, such strategycannot easily find implementation in an unsupervised home setting by anactual hearing aid user.

In a more recent, and very limited selection strategy, geneticalgorithms were blended with user input to achieve a hearing aidfitting. As is known, and as its name implies, genetic algorithms, firstintroduced by John H. Holland, are a class of algorithms modeled uponliving organisms' ability to ensure their evolutionary success vianatural selection. In natural selection, the fittest organisms survivewhile the weakest are killed off. The next generation of organisms(children) are, thus, offspring of the fittest previous generation(parents). The algorithms also provide for mutations as insuranceagainst the development of a relatively unchanging population incapableof continued evolution.

In breeding children or offspring in a genetic algorithm, “crossover”operators are applied to parent genes. In essence, two parent bitstrings (ones and zeroes, for example) from the algorithm are crossed ata crossover point and the children are given attributes of each parent.Mutation operators are also applied to a relatively smaller number ofparent bit strings, typically by replacing ones with zeroes and viceversa. Both crossover and mutation closely model biological behaviorwhere parent chromosomes line up and crossover thereby swapping portionsof their genetic code or become mutated.

The determination of which children are the results of which parents,how many children are produced, how many children survive, how longparents survive, how many mutations per children are created and othersimilar algorithm manipulations are functions of each particular geneticalgorithm and vary, probably, as widely as the number of geneticalgorithms in use.

In this particular hearing aid selection strategy using geneticalgorithms, human subjects were asked to rank 20 hearing selections on ascale of 1 to 5. Then, through a series of genetic algorithmcomputations, a winning hearing selection was converged upon.

With absolute scaling approaches of this type, however, humans aregenerally not able to maintain the same response criteria over such awide number of listening trials. For example, what a subject mightrecord as a 2 for the first selection might not be the same 2 recordedfor the twentieth selection. In other words, the scaling makes thecomparison selection too complex. Moreover, and as with all hearing aidfitting selection strategies, this approach is unrealistic for hearingaid users to implement in their home in an unsupervised setting.

In a broader setting, genetic algorithms have also seen application inother perceptual tuning environments. For example, they have been usedto (interactively with human subjects) tune simulated automobile windnoise to the subject's satisfaction and to successfully fit head-relatedtransfer functions. These activities, like hearing aid fittings, takeplace in research settings and cannot, even if it were desirable, bereadily performed in unsupervised field settings.

In a still broader setting, some genetic algorithm operators (crossoverand mutation), have typically ineffectively evolved an organisms'population because of quickness, slowness, unstableness or some otherpoorly performing process in the operators. This is because theoperators themselves typically operate directly on bit strings ordirectly on parameters having a wide, varied and non-linear range.

Accordingly, the art needs a better and more simple selection strategyfor fitting or tuning hearing aids to individual users' preferredsettings. Preferably, it needs an unsupervised field settingimplementation. In a broader setting, the art needs better geneticalgorithms for perceptually tuning a system having many interactingparameters. Still even more broadly, the art needs better geneticalgorithm operators that serve to better evolve populations.

SUMMARY OF THE INVENTION

The above-mentioned and other problems become solved by applying theapparatus, method and system principles and teachings associated withthe hereinafter described hearing aids and audio fitting thereof. Evenfurther, by applying the principles and teachings described for geneticalgorithm operators, better genetic algorithms can be applied toperceptually tuning any system, such as a radio, a hearing aid, apersonal data or digital assistant device, etc.

In one embodiment, a field ready, unsupervised-use ready, method andapparatus for fitting a hearing aid is described in a hand heldconfiguration having paired comparisons (hearing selections) stored inand derivable from a memory therein. The paired comparisons arepresented one at a time to a user and a preferred selection for eachpaired comparison is made by a select indicator after the user togglesback and forth between the selections for as many times necessary indetermining his or her preferences. A genetic algorithm converges allthe preferences upon a single solution. Crossover and mutation geneticalgorithm operators operate on a linear range of indexes representativeof parametric values of the pairs. A fully integrated hearing aid havingall the above described features incorporated therein is also presented.

In still another embodiment, a genetic algorithm for perceptually tuninga system is presented that converges upon a solution set from aplurality of parent and child sets in first and second populations thatwere presented to a user in a paired comparison format.

In still other embodiments, crossover and mutation genetic algorithmoperators are described that neither operate directly on a parametricvalue nor a string of bits representing the parametric value, but on alinear range of indexes representative of parametric values used by aparent from a population.

These and other embodiments, aspects, advantages, and features of thepresent invention will be set forth in part in the description thatfollows, and in part will become apparent to those skilled in the art byreference to the following description of the invention and referenceddrawings or by practice of the invention. The aspects, advantages, andfeatures of the invention are realized and attained by means of theinstrumentation, procedures, and combinations particularly pointed outin the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagrammatic view of a perceptual tuning system inaccordance with the teachings of the present invention showing a hearingaid user and apparatus useful in an audio fitting thereof;

FIG. 1B is a diagrammatic view of a perceptual tuning system inaccordance with the teachings of the present invention showing a hearingaid user and apparatus useful in an audio fitting thereof in a wirelessembodiment;

FIG. 2 is a block diagram in accordance with the teachings of thepresent invention for the system of FIG. 1 (FIG. 1A or FIG. 1B);

FIG. 3A is a diagrammatic view in accordance with the teachings of thepresent invention showing a first population comprised of a plurality ofparent sets;

FIG. 3B is a diagrammatic view in accordance with the teachings of thepresent invention showing a genetic algorithm crossover operator;

FIG. 3C is a diagrammatic view in accordance with the teachings of thepresent invention showing a genetic algorithm mutation operator;

FIG. 3D is a diagrammatic view in accordance with the teachings of thepresent invention showing a creation of a mutation set for the mutationoperator of FIG. 3C;

FIG. 3E is a diagrammatic view in accordance with the teachings of thepresent invention showing a second population comprised of a pluralityof parent and child sets;

FIG. 4A is a first portion of a flow diagram in accordance with theteachings of the present invention showing paired comparisons presentedto a user for selection thereof;

FIG. 4B is a second portion of a flow diagram in accordance with theteachings of the present invention showing paired comparisons presentedto a user for selection thereof; and

FIG. 5 is a block diagram of an alternate embodiment of a perceptualtuning system in accordance with the teachings of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following detailed description of the preferred embodiments,reference is made to the accompanying drawings which form a part hereof,and in which is shown by way of illustration, specific embodiments inwhich the inventions may be practiced. These embodiments are describedin sufficient detail to enable those skilled in the art to practice theinvention, and it is to be understood that other embodiments may beutilized and that process, electrical or mechanical changes may be madewithout departing from the scope of the present invention.

With reference to FIG. 1A, a perceptual tuning system of the presentinvention is shown generally as 10. The system, as presented in thisfigure and the remaining description, is in the context of fitting ahearing aid for a sensorineurally impaired user. It will be appreciated,however, that the system may and should be extended to various otherenvironments, such as tuning a radio, a personal data assistant or anyof a number of devices requiring such tuning. Thus, the presentinvention is not expressly limited to a hearing aid fitting unless sodefined in the claims.

As illustrated, the system 10 has a user 12 outfitted with a hearing aid14, an apparatus 16 in a hand held configuration for audio fitting thehearing aid via user selection of paired comparisons stored in andderivable therefrom and a communications link 18 in between. In oneembodiment, as depicted by FIG. 1B the communications link 18 is awireless link and the necessary communications hardware are found inapparatus 16 and hearing aid 14 to support the wireless link. Apparatus16 is a self-contained device ready for field use (e.g., home use) in anunsupervised setting.

It will be further appreciated that the system of FIG. 1A (or FIG. 1B)is shown as a left hearing aid configuration and one skilled in the artwill be readily able to adapt the teachings herein and apply themwithout undue experimentation to right hearing aid embodiments and tosystems having both left and right hearing aid embodiments. As such, theclaims of the present invention are not to be construed as limited to asingle, left hearing aid configuration.

It will be even further appreciated that hearing aids, although alwayshaving analog components, such as microphones and receivers, aregenerally referred to according to their primary mode of signalprocessing (analog processing or digital signal processing (DSP)) andcan be of any type as described herein. The claims, therefore, are notto be construed as requiring a specific type of hearing aid.

Still further, although not shown, the present invention may findapplicability in contexts in which an audiologist uses apparatus 16 toassist user 12 in fitting hearing aid 14.

With reference to FIG. 2, the apparatus 16 and hearing aid 14 of system10 are representatively shown in block diagram format and will bedescribed first in terms of their electro-mechanical interconnections.Thereafter, and with simultaneous reference to other figures, theapparatus and heating aid of system 10 will be described in functionaldetail. In the embodiment shown, apparatus 16 includes fully integrateduser interface 20, processor 22 and power supply 23 for providingnecessary voltage and currents to the user interface and processor. Inan alternative embodiment, the apparatus 16 is separated into discretecomponents and/or discrete/integrated hybrids connected by appropriatecommunications links between the functional blocks with common ordiscrete internal or external power supplies.

User interface 20 may include volume switches 24, 26, respectively, forincreasing (+) or decreasing (−) a volume of the apparatus 16 asappropriate. Select indicator 28 is used to indicate user preferencebetween paired comparisons. Toggle device 30 allows the user to toggleback and forth between paired comparisons as often times as necessarybefore indicating their preference. The actual presentment of pairedcomparisons and indication of preference will be described in much moredetail below.

The volume switches 24, 26, the select indicator 28 and toggle device 30may be any of a variety of well known integrated or discrete switches,slides, buttons, etc. They preferably include electro-mechanicalswitches that send electrical signals in response to a mechanicalmanipulation thereof. They preferably have appropriate size and shape toenable users to comfortably and intuitively manipulate them with verylittle manual dexterity.

In another embodiment, the toggle device 30 is not a mechanical deviceto be manipulated by a user but a software algorithm stored in processormemory that automatically toggles between paired comparisons accordingto a preferred timing schedule.

Visual indicators 32 of varying number, color and pattern are alsopreferably provided in the form of lights, such as light-emitting diodes(LED) to provide immediate visual feedback to the user upon manipulationof one of the user inputs.

Connected to the user interface 20 is processor 22 having a centralprocessing unit 34, preferably a DSP with internal on-chip memory,read-only memory (ROM) 36 and flash memory 42 for use as a logging spaceof the user inputs from user interface 20.

ROM 36 preferably includes at least two algorithms. Hearing aidalgorithms 38 and genetic algorithms 40.

In a fashion similar to that of the apparatus itself, it should beappreciated that processor 22 may be a fully integrated device orcomprised of discrete components or a discrete/integrated hybrid andthat all such embodiments are embraced herein.

The foregoing apparatus 16 is connected at one end of the communicationslink 18. At the other end is the hearing aid 14. In one embodiment, thecommunications link 18 is a set of wire(s). In an alternate embodiment,the link 18 is wireless. The link 18 in such embodiments includes, butis not limited to, any well known or hereinafter developedcommunications scheme, modulated or un-modulated technologies,including, but not limited to, wireless radio frequencies, infraredtransmitter/receiver pairs, Bluetooth technologies, etc. In suchembodiments, suitable hardware/software processing devices would becontained in the apparatus 16 and the hearing aid 14.

As shown, the hearing aid 14 contains an initial prescription setting48, a microphone 44, a receiver 46 and a reset mechanism 50. It will beappreciated the hearing aid also contains other mechanisms that are notshown but are well known to those skilled in the art, such as a powersupply and a signal processor.

In one embodiment the apparatus 16 and hearing aid 14 are discretecomponents. In another embodiment, the entire contents of apparatus 16and hearing aid 14 are fully integrated into one single hearing aidpackage 52.

Before describing the functional operation of the apparatus 16 togetherwith hearing aid 14, or, alternatively, completely integrated hearingaid package 52, some words and nomenclature as used throughout thisspecification are presented. A “parameter” as used herein relates to acharacteristic element of the system 10 that can take on a discretevalue. In some embodiments, the discrete value is selected from one of arange of values. In one embodiment, for example, a parameter of FilterLength, L, (in # of filter taps) the discrete parametric value is 9. Itis understood that the parameter L is not limited to a particular valueof 9 and can be another number. The parameter L is capable of being anyof the discrete values, including, but not limited to, 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 13, 16, 20, 25, 32, 40, etc. In one embodiment, the filterlength L may be as short as 1 (mere scaling of the input) and as long256. The parameter L may be a discrete value taken from a range ofcountable numbers, for example, {3, 4, 5, 6, . . . , N or Infinity}. Theparameter L may also be a discrete value taken from an irregular set,such as {8, 10, 13, . . . , 32, 40}, for example. Other range types andranges are possible, and the examples given here are not intended in alimited or exclusive sense. Typically what constrains the upper limit isthe size of available memory, processing speed and the ability of a userto discern differences in that many filter taps. Some particularexamples of parameters for perceptually tuning a hearing aid may be, butare not limited to, any of the following terms well known to researchaudiologists and audio processing engineers skilled in the art: gain,compression ratio, expansion ratio, frequency values, such as samplingand crossover frequencies, time constant, filter length, compressionthreshold, noise reduction, feedback cancellation, output limitingthreshold, compression channel crossover frequencies, directional filtercoefficients, constrained representations of large parameter groupings,and other known or hereinafter considered parameters. A “set” as usedherein is one or more parameters. A “population” is a plurality of sets.Capital letters A, B, C, D, . . . X, . . . etc., having subscripts orsuperscripts or both therewith will either be a particular parameter,such as A₁ or A′₁, or a particular set, such as set A, set A′, set B,set C, . . . set X, . . . etc. and will be understood from the contextin which they are used. Numerous sets and sets of sets will behereinafter presented. For clarity, they will often be presented incombination with reference to any of a variety of terms such as“parent,” “child,” “mutation,” or “summation.” These particular types ofsets will also be understood from the following discussion.

With reference to FIG. 3A, a population 320 is shown in tabularpresentation 310 as a plurality of sets, set A, set B, set C, set D, . .. set X. Each set has, as defined, one or more parameters 1, 2, 3, . . .n (i.e., n≧1) wherein, for example, parameter 1 for set A is shown as A₁while parameter 3 for set C is shown as C₃ etc. It will be appreciatedthat the depiction of a population in the foregoing manner is merelyillustrative to enhance the reader's understanding of the presentinvention and does not require a physical creation of the population,nor is it required to be created in any particular format or groupings.As defined, it needs to be a plurality of sets.

With simultaneous reference to the many figures, a preferred embodimentof the present invention will be described that illustrates the methodsfor utilizing user selection between paired comparisons, and the geneticalgorithms used to process the selections, in fitting hearing aids. Atstep 410 of FIG. 4A, a population 320 is created. For clarity, sincemultiple populations are set forth hereinafter, this population will bea first population. The sets A, B, C, D, . . . X shown in population 320will be referred to as parent sets. This first population is stored inany of the memories of the processor 22.

At step 412 a first pair of parent sets from the population 320 isselected for presentation to the user 12. This first pair can be any twosets of the parent sets of the first population and are preferably (butnot necessarily) selected via the genetic algorithm to be describedbelow. For example, the first pair consists of parent set A and parentset B. This step is invisible to the user and is performed very rapidlyin processor 22.

At step 414, the first pair of parent sets is presented to the user.Presentation of the first pair of parent sets, it should be appreciated,means presentation of one parent set at a time, either parent set A orparent set B, for example. Presentation in the perceptual tuningenvironment of a hearing aid is accomplished via hearing aid 14. It willbe further appreciated that since the parent sets of population 320 arecomprised of one or more parameters, and since hearing aids have manyparameters used to process sound, the user while being presented a pairof parent sets is actually being presented with a set of parametersthrough which they “hear” sounds. The user does not actually hear theparameters, they merely hear sounds in their hearing aid as processedvia the parameter sets in parent set A or parent set B. Usually, hearingaids have appropriate and proprietary software to process sounds, suchas hearing aid (H.A.) algorithm 38 stored in ROM 36 of apparatus 16.

In one actual experiment performed by the inventor of the presentinvention, parent sets were presented to a user in order to cancelfeedback in the hearing aid. The sets were comprised of three parametersand two of the parameters were selected from the list consisting ofFilter length, L, and time constant, α. Each of these parameters aresummarized in the following table, Table 1.

TABLE 1 Exemplary Parametric Values Used in Experimental Testing L,Filter Length (# of filter taps) 8 10 13 16 20 25 32 40 α, Time Constant10 14 19 27 37 52 72 100 (×10⁶) (1/sec)

It should be appreciated that parameter Filter Length, L, for example,is one of any 8 discrete values (8, 10, 13, 16, 20, 25, 32, 40) arrivedat via experimental data and is not to be considered limiting of thescope of the present invention.

Eventually, the user will need to determine which parent set they likebetter. Since a user can only “hear” one parent set at a time, eitherparent set A or B, for example, the user will need to toggle, at step416, to the other parent set. Toggling is accomplished via depressing oftoggle device 30.

To assist the user in determining which parent set they like better,they should perform a variety of tasks pertinent to the tuning. In theactual experiment, users were asked to “listen” to the parent sets in avariety of environments prone to feedback, such as placing their handover their ear(s), listening to telephone dial tones, performing jawmovements, listening in the presence and/or absence of other backgroundnoises, and other similar activities.

At step 418, after performing such tasks, can the user decide whichparent set of the first pair they prefer? If they cannot, they merelyrepeat steps 416 and 418 for as many times necessary until they canindicate a preference. If they can, the user indicates a preference bydepressing select indicator 28 whereupon the result is logged-in thelogging space of flash memory 42. The logging space, for example, can bearranged in many ways. In a preferred embodiment, it can be arranged torecord that parent set B was preferred over parent set A, such that an Aor a B are recorded in a memory address pointing to the parameters fromthe parent sets that are actually part of parent sets A and B. Whetherthis type of logging is performed or another, they are to be embraced bythis invention. All types of memory recording are well known writetechniques.

At step 420, a second pair of parent sets from the first population isselected for presentment to the user. In continuing the example, parentset C and parent set D are selected via the genetic algorithm 40 storedin ROM 36.

One at a time, one of parent sets C or D is presented to the user atstep 422 and the user toggles to the other of the parent sets at step424. If the user cannot decide which of the parent sets of the secondpair they prefer (step 426), they toggle back and forth between thesecond pair of parent sets.

Thereafter, the parent sets are ranked at step 428. A hierarchicalranking is determined for each parent set included in the presentationto the user. For example, parent sets A, B, C and D were presented tothe user as first pair parent set A and B and second pair parent set Cand D.

If the user preferred parent set B over A and parent set D over C, theranking would be as follows: parent set B

parent set A; and parent set D

parent set C with the relationships between, at least the two preferredparent sets {B, D} and the two non-preferred parent sets {A, C} beingunknown. As such, it is likely that more than two iterations ofpresentment of pairs to the user would be performed. How many iterationsdepends upon how large the population is and what inferences can be madetherefrom as will be shown in the continuing example. It should beappreciated that such description regarding how many pairs are presentedis part of the work of the genetic algorithm 40 preferably stored in ROM36 of apparatus 16. As such, no exact number of paired comparisons isrequired.

Intuitively, with fewer than four parent sets, fewer iterations arenecessary. For example, if the population only consisted of two parentsets, only one pair of paired comparison need be presented before it isunderstood which parent set is preferred over all other sets and whichset is preferred least amongst all other sets.

To continue, if parent set B and parent set D were presented to the userfor paired comparison and the user indicated a preference for parent setB over D, we would also have: parent set B

parent set D.

Or, graphically:

At this point, although parent set B and parent set C were neverpresented in direct comparison to one another, it can be inferred thatparent set B

parent set C. If you next presented parent set A and parent set D, andparent set D is indicated as preferred over parent set A, the geneticalgorithm knows, and can more meaningfully receive a ranking (step 428):parent set B

parent set D

{A, C} where the relationship between parent set A and C is unknown. Ina preferred embodiment, the ranks given are parent set B equal to number1, parent set D equal to two, and parent sets A and C equal to 3.5 or((3+4)/2) since they “tied.” The advantage of the foregoing is thatinconsistencies are avoided by not making comparisons when thedominance-equivalence relationship can be inferred from previousresponses (parent set B “dominates” C, parent set A is “equivalent” toC, for example).

With genetic algorithms, as in biology, the least fit genes do notsurvive. As such, the relationship between parent sets A and C isunnecessary to determine.

After ranking, probabilities of selection (as preferences are indicatedvia the select indicator 30) are assigned to the ranked parent sets atstep 430.

In this example, the probabilities are presented in Table 2.

TABLE 2 Exemplary Probabilities of Selection of Parent Sets Parent Set(₁) Probability of Selection, p₁ A 0.19 B 0.36 C 0.19 D 0.26

As will be noticed, the probabilities add up to equal one with higherprobabilities assigned to those parent sets with higher ranks, i.e.,those parent sets more likely to be selected by the user in a pairedcomparison over another parent set. Accordingly, parent set B has thehighest probability while parent sets A and C have the lowest.

At step 434, the weakest, or least fit, parent sets of the firstpopulation will be replaced with child sets. In arriving at the childsets, it must first be determined which of the parent sets are thefittest so that the fittest survive while the weakest die. From thetable, parent sets B and D are the fittest simply because they have thehighest probabilities of selection while parent sets A and C are theweakest.

Once the two fittest parent sets are determined, genetic algorithmoperators, either crossover and/or mutation at step 432 are applied toat least one parent set to produce child set(s). In one embodiment, thetwo fittest parent sets could be selected to produce child sets. Inanother embodiment to determine which parent sets will be used toproduce a child set, two unique fittest sets of the parent sets areselected. This is done by associating each of the parent sets withintervals of numbers and, depending upon the comparison of intervalswith the output of a random number generator between zero and one (suchas that provided by a uniform random number generator), selecting thetwo unique fittest parent sets.

For example, parent set A is associated with the interval of numbers [0,p_(A)] or from Table 2, [0, 0.19], parent set B the interval (p_(A),p_(A)+p_(B)] or (0.19, 0.55], parent set C the interval (p_(A)+p_(B),p_(A)+p_(B)+p_(C)] or (0.55, 0.74], and parent set D (p_(A)+p_(B)+p_(C),p_(A)+p_(B)+p_(C)+p_(D)] or (0.74, 1.0]. Next, in the actual experiment,a random number generated two outputs. A 0.95 followed by a 0.23. Thus,the two unique fittest parent sets are parent sets D and B (i.e, 0.95 isfound in the range of the interval for parent set D (0.74, 1.0] while0.23 is found in the range of the interval for parent set B (0.19,0.55]). Thus, parent sets B and D are the two unique fittest sets andwill be used to produce child sets.

To illustrate both crossover and mutation, a table of indexes will becreated from the exemplary parametric values from Table 1. In devisingthis table of indexes, however, only the filter length parameter will beused. It will be appreciated, though, that index tables for allparameters could be constructed. In this instance, parameter filterlength, L, has 8 values (8, 10, 13, 16, 20, 25, 32 and 40) from Table 1.This parameter range reflects a wide range of values and it should beappreciated that it is non-linear. Then, an index is assigned for eachvalue. Sine there are eight values, eight indexes or indexes areassigned. In this case, indexes in a parent index range from 0 -7 wereselected with 0 being the low parent index and 7 being the high parentindex. The result is shown in Table 3.

TABLE 3 Exemplary Table of Indexes for Filter Length, L, from Table 1.Index 0 7 (Low) 1 2 3 4 5 6 (High) L, Filter Length 8 10 13 16 20 25 3240

Then, with reference to FIG. 3B, the genetic algorithm crossoveroperator will be described as acting on the two fittest parent sets fromthe previous example (parent set B and D which are also the two uniquefittest sets). In the figure, parent set B is comprised of a pluralityof parameters 1 through n as shown by B₁ through B_(a). In this example,n will equal 4. Likewise, for parent set D.

Parameter 1 for parent set B is a filter length of 16. Parameter 2 forparent set B is a filter length of 20. Parameter 3 for parent set B is afilter length of 10. Parameter 4 for parent set B is 32. Instead ofshowing parent set B as a set of parameter values, it is shown as a setof indexes. Thus, parent set B is shown as:

3 4 1 6

where 3, 4, 1, and 6 are the parent indexes corresponding to therespective filter lengths 16, 20, 10, and 32. In a similar fashion,parent set D has parent indexes of 0, 6, 7, and 3.

In a first step for performing crossover, select a parent index position100 and a child index position 102. In this embodiment, both positionsare the same and correspond to the third index position (from the left)in both parent sets B and D (i.e., index 1 for parent set B and index 7for parent set D) and the child index position 102 in both child sets B′and D′.

Then, create indexes in the child set B′ by:

i) reproducing in child set B′, to a left of the child index position,the parent indexes of one of the parent sets (parent set B) to the leftof the parent index position (in this case indexes 3 and 4);

ii) reproducing in child set B′ to a right of the child index position,the parent indexes of the other of the parent sets (parent set D) to theright of the parent index position (in this case index 3); and

iii) at the child index position, create any child index that issubstantially equal to the low parent index (0), the high parent index(7) or any index in the range of indexes from the low parent index (0,from Table 3) to the high parent index (7, from Table 3). Likewise forchild set D′.

In another embodiment, step iii) comprises, at the child index position,selecting the indexes from parent sets B and D (index 1 and index 7,respectively) and using a random number to select two new child indexes(one for each child set) in the range of between index 1 (low parentindex) and index 7 (high parent index) that sum to the same value as thesum of the two parent set indexes, in this case, 1+7=8. For example, thetwo new child indexes for child sets B′ and D′ respectively, which allsum to the value 8, include (1,7), (2,6), (3,5) (4,4), (5,3), (6,2) and(7,1).

For mutation, with reference to FIG. 3C, begin with a parent set (parentset B). Again, the parent set is comprised of parameters beingrepresented by parent indexes 3, 1, 6, and 3. Next, create a mutationset having a plurality of mutation indexes. In this example, theyinclude −1, 0, 3, and 0.

Sum the mutation indexes and the parent indexes of the mutation andparent sets to form a summation set having a plurality of summationindexes. In this example, 2, 1, 9, 3 or (3+−1), (1+0), (6+3), (3+0).

Finally, reproduce the summation indexes in a child set. Since thesummation indexes in this example had one index, the number 9, outsidethe range of the parent indexes from low parent index 0 to high parentindex 7, the index 9 was rounded to the closest parent index in therange. In this example, the index 9 was rounded to 7 and the child setB′ became child indexes 2, 1, 7, and 3.

In determining the mutation set, it is preferred to use a random numbergenerator to come up with the mutation indexes. With reference to FIG.3D, it will be appreciated that the mutation indexes −1, 0, 3, and 0were obtained by respectively rounding random numbers −0.94, 0.22, 3.49,and 0.18 to the nearest positive or negative integer.

In a preferred embodiment, the random numbers −0.94, 0.22, 3.49, and0.18 were obtained from a normal distribution, N(0, σ²) for randomvariables having an average of 0 and a variance of distribution beingequal to σ² where σ=(a system constant×the number of indexes in therange of parent indexes). In this example, the system constant is 0.2which was determined empirically by various run simulations of numberswhile the number of indexes in the range of parent indexes 0-7 is eight(8): σ=0.2×8=1.6.

Mutation indexes could also be arrived at by various other randomnumbering schemes well known in the art. They could even be obtainedwithout regard to random numbering. It is believed, however, that randomnumbers make the system more robust.

It should be appreciated that in the foregoing, the table of indexes islinear while the parametric values represented by the table of indexesis non-linear. In this manner, it has been observed that betterevolution of populations occurs as compared to other evolution schemesthat crossover and mutate directly on bit strings or directly onparameters having a wide, varied and non-linear range. Unlike thoseschemes where evolution is often too quick, too slow, too unstable orsome other poorly performing process, the evolution of the presentinvention is stable and robust.

It should also be appreciated that use of the foregoing describedoperations for performing crossover and mutation, ultimately, allowsmixing and matching of different parameter sets having different unitsand measuring schemes.

Thereafter, at step 434, the least fit or the weakest parent sets of thefirst population are replaced with child sets obtained from mutation,crossover or both. For example, in FIG. 3E a second population 322results, presented in tabular form 310, having weak parent sets A and Cof the first population replaced with child sets B′ and D′ from thecrossover operation of FIG. 3B.

Thereafter, the steps beginning at step 410 of presenting pairedcomparisons to users begin again. It should be appreciated, however,that when comparisons are made now, they are made from the sets of thesecond population. As such, parent sets may be compared exclusively toother parent sets, child sets compared exclusively to other child sets,or a hybrid comparing child sets to parent sets.

As this process repeats itself, numerous population sets may be evolvedthat are many generations removed from the first population. The presentinvention is not limited to any particular number of populationgenerations.

It should be further appreciated that as generations evolve (morepopulations are created), the relationships in the previous populationare already known so that the only relationships that need to bedetermined are those between the child sets and the child sets withparent sets. As such, fewer comparisons are required with each nextpopulation. This results in less time being required for each nextpopulation.

How often mutation and crossover occur in relation to one another is afunction of user preference. In one embodiment, more crossovers happenearly on with more mutations happening later or vice versa. In anotherembodiment, crossovers and mutations occur together in the same exactnumber. In still another embodiment, crossovers exceed mutations, orvice versa.

The process described above repeats itself for as many times asnecessary to arrive at a converged upon solution set having thepreferred set of parameters for fitting the hearing aid to theparticular user. For example, it may be discovered that child set B′ isthe preferred set of all sets presented.

Consequently, it should be appreciated that users do not have tomaintain consistent application of numbering scales, such as from 1 to5, in their minds as they apply them to numerous various hearingsolutions. They simply need to indicate a preference for one element ofeach pair (of hearing aid settings). This is as simple as depressing aselect indicator on a hand held device after toggling back and forthbetween the selections for as many times necessary in determining theirpreferences. A genetic algorithm does all the computation and kills offpoor (weak) hearing solutions thereby quickly converging all thepreferences upon a single solution. In the actual experiment, users madeapproximately 50 to 75 paired comparisons during about one hour oflistening.

Moreover, the present embodiments now advantageously provide a solutionwhereby field ready (in home), unsupervised use ready hearing aids canbe fit without need of any input other than the user's.

In another embodiment, the initial prescription setting 48 can be resetvia mechanism 50 to update or replace the original prescription with theparameter(s) of the converged upon solution set. In this manner, thehearing aid prescription can be modified according to individualpreferences in the user's home environment. Preferably, reset mechanism50 is a read/write device that can read the parameters of the solutionset and write them over the parameters of the initial prescriptionsetting.

With reference to FIG. 5, another manner in which perceptual tuning inaccordance with the present invention can be perceived is showngenerally as 500. In this diagram, the system 528 would be the hearingaid 14 of the previous example.

The population 518 is the same as population 320 before. The partialdominance model contains an entry for each possible pair, withoutrespect to presentation order, from population 518 that can be presentedto the user. For example, parent set A can be presented with parent setsB, C, D, . . . or X. Likewise, parent set B can be presented with parentsets C, D, . . . or X, etc. With 5 parent sets, 10 possible pairsresult.

The parent sets are presented to the user as the block “select andpresent pairs” 524. Once presented, the user indicates his/herpreference and the preference is logged at data collection 534 alongwith which two parent sets were presented and what parameters they wereconstructed from.

The user bases his/her preference upon the perception 532 of the pair(of parent sets) as they are observed in environment 530. As is impliedby their names, perception is the act or result of perceiving as done bythe user and environment is the surroundings in which the sounds areperceived.

The one or more parameters from which the parent sets are constructedare contained in block 526, set of n parameters, with n being one ormore. Again, the parameters are a function of the system in which theyare used and therefore interact diagrammatically with the system 528.

The hierarchical ranking of the parent sets happens with conversion toranks 516.

At 514, a hypergeometric fitness function assigns the probabilities ofselection based upon the conversion to ranks 516.

Based on this information, the genetic algorithm 512, decides whichpairs, if any, from the population 518 become presented to the users at524. Non-binary GA operators such as mutation and crossover previouslydescribed supply input to the genetic algorithm to form child sets.Thereafter, the genetic algorithm replaces parent sets of the population518 with newly created child sets to form a second population comprisedof parent sets and child sets. The genetic algorithm also decides whichsets from this second population get presented to the user at 524.

In further detail, the method of presenting pairs of parent sets andinferring ranks has four components. The first chooses which pair getspresented to the user. Then, the second draws all possible inferencesbased on the user's response and previous information. The thirddetermines if another comparison is required. If another response is notrequired, the fourth is invoked and generates ranks for all the membersof the population.

The data collection can also include information about environment 530that can be provided to the genetic algorithm to assist in its selectionof pairs for presentment and, ultimately, upon its convergence upon asolution set that perceptually tunes the system.

Finally, and representative of all embodiments herein, computer readablemedium which can be accessed by a special or general purpose computercould be used to store information thereon, such as the geneticalgorithm, the crossover operator, the mutation operator, thehypergeometric fitness function, the conversion to ranks, thepopulation(s), the partial dominance model, and/or the parameters and/orother methods described that give way to being read by a computer. Byway of example, and not limitation, such computer readable media cancomprise ROM, RAM, EEPROM, CDs or other optical disk storage devices,floppy disks or other magnetic disk storage devices, or any other medianow known or hereinafter invented which can be used to store the desiredexecutable instructions or data fields of the exemplary informationabove. In a preferred embodiment, the stored information will be loadedinto and for use in either the apparatus 16, the fully integratedhearing aid package 52 and/or the hearing aid 14. While these computerreadable media are not shown in any figure, they are not required to befor they represent technologies well known to those skilled in the artand their description is not any better understood by referencing aparticular figure.

Conclusion

Hearing aids and methods and apparatus for efficaciously audio fittingthem have been described. More broadly, however, the perceptual tuningof any system has been described that uses genetic algorithms that, inturn, utilize user input selection from paired comparisons. Such userinput selection from paired comparisons in an audio fitting of a hearingaid, for example, relates to a user comparing two audio settings andselecting a preferred one. Still even more broadly, the presentinvention has been described in terms of improved genetic algorithmcrossover and mutation operators for use in a genetic algorithm thatneither operate directly on a parametric value nor a string of bitsrepresenting the parametric value.

The present invention has been particularly shown and described withrespect to certain preferred embodiment(s). However, it will be readilyapparent to those of ordinary skill in the art that a wide variety ofalternate embodiments, adaptations or variations of the preferredembodiment(s), and/or equivalent embodiments may be made withoutdeparting from the intended scope of the present invention as set forthin the appended claims. Accordingly, the present invention is notlimited except as by the appended claims.

What is claimed is:
 1. An apparatus for fitting a hearing aid,comprising: a memory having a first population stored therein, the firstpopulation comprising a plurality of parent sets; a toggle device fortoggling between a first pair of the plurality of parent sets; a selectindicator for selecting a preferred one set of the first pair; and acommunications link for interfacing with the hearing aid.
 2. Theapparatus according to claim 1, wherein each parent set of the pluralityof parent sets comprises more than one parameter.
 3. The apparatusaccording to claim 1, wherein the communications link is a wirelesslink.
 4. The apparatus according to claim 1, further comprising aprocessor for ranking a hierarchy of the plurality of parent sets. 5.The apparatus according to claim 1, further comprising a processor forassigning a probability of selection by the select indicator to theplurality of parent sets.
 6. The apparatus according to claim 1, whereinthe plurality of parent sets comprises at least a first, second andthird set, further comprising a genetic algorithm for deciding which ofthe first, second and third sets becomes the first pair.
 7. Theapparatus according to claim 1, further comprising a genetic algorithmoperator for performing one of mutation and crossover on at least oneset of the plurality of parent sets thereby forming a child set.
 8. Theapparatus according to claim 7, further comprising a genetic algorithmfor replacing one of the plurality of parent sets in the firstpopulation with the child set thereby forming a second population. 9.The apparatus of claim 8, wherein the toggle device toggles between asecond pair of sets selected from the second population.
 10. Theapparatus of claim 1, wherein the toggle device toggles between aplurality of pairs of the plurality of parent sets, further comprising aprocessor for converging the plurality of pairs to a single solutionset.
 11. A hearing aid fitted by the apparatus according to claim
 1. 12.An apparatus for fitting a hearing aid, comprising: a memory having afirst population stored therein, the first population comprising aplurality of parent sets; a toggle device for toggling between aplurality of pairs of the plurality of parent sets, each of theplurality of pairs having a first and second set; a select indicator forselecting a preferred one of the first and second set in the each of theplurality of pairs; a genetic algorithm operator for performing one ofmutation and crossover on at least one set of the plurality of parentsets thereby producing a child set; a genetic algorithm for replacingone of the plurality of parent sets in the first population with thechild set thereby forming a second population wherein the toggle devicetoggles between another pair of sets, the another pair being selectedfrom the second population; a processor for converging the sets of theplurality of pairs and the another pair to a single solution set; and acommunications link for interfacing with the hearing aid.
 13. Theapparatus according to claim 12, wherein the processor ranks a hierarchyof the plurality of parent sets.
 14. The apparatus according to claim13, wherein the processor assigns a probability of selection by theselect indicator to the sets of the plurality of pairs and the anotherpair.
 15. The apparatus according to claim 14, wherein the geneticalgorithm decides which of the plurality of parent sets becomes thefirst and second sets for each the plurality of pairs.
 16. A hearing aidfitted by the apparatus according to claim
 12. 17. A hearing aid,comprising: a memory having a first population stored therein, the firstpopulation comprising a plurality of parent sets, each of the parentsets having at least one parameter; a toggle device for toggling betweena first pair of the plurality of parent sets; and a select indicator forselecting a preferred one set of the first pair.
 18. The hearing aidaccording to claim 17, further comprising an initial prescriptionsetting and a reset mechanism for updating the initial prescriptionsetting with one of the at least one parameters.
 19. The hearing aidaccording to claim 17, wherein the toggle device is one of a softwarealgorithm and mechanical mechanism.
 20. The hearing aid according toclaim 17, further comprising a processor for ranking a hierarchy of theplurality of parent sets.
 21. The hearing aid according to claim 17,further comprising a processor for assigning a probability of selectionby the select indicator to the plurality of parent sets.
 22. The hearingaid according to claim 17, wherein the plurality of parent setscomprises at least a first, second and third set, further comprising agenetic algorithm for deciding which of the first, second and third setsbecomes the first pair.
 23. The hearing aid according to claim 17,further comprising a genetic algorithm operator for performing one ofmutation and crossover on at least one set of the plurality of parentsets thereby forming a child set.
 24. The hearing aid according to claim23, further comprising a genetic algorithm for replacing one of theplurality of parent sets in the first population with the child setthereby forming a second population.
 25. The hearing aid according toclaim 24, wherein the toggle device toggles between a second pair ofsets selected from the second population.
 26. The hearing aid accordingto claim 17, wherein the toggle device toggles between a plurality ofpairs of the plurality of parent sets, further comprising a processorfor converging the plurality of pairs to a single solution set.
 27. Amethod of fitting a hearing aid, comprising the steps: preparing a firstpopulation of a plurality of parent sets; presenting a first pair fromthe parent sets, the first pair comprising a first and second set andbeing presented with assistance of the hearing aid; selecting a firstpreference between the first and second sets of the first pair;operating on at least one set of the plurality of parent sets to obtaina child set, the child set being one of a mutation and crossover;replacing one of the plurality of parent sets of the first populationwith the child set to form a second population; presenting a secondpair, the second pair comprising the child set and a third set, thethird set being selected from the second population but not being thechild set; selecting a second preference between the child set and thethird set of the second pair; and converging on a solution set, thesolution set being one of the first, second, third and child sets. 28.The method according to claim 27, further comprising the step of rankinga hierarchy of the plurality of parent sets.
 29. The method according toclaim 27, further comprising the step of assigning a probability ofselection in one of the selecting steps to the first, second, third andchild sets.
 30. The method according to claim 27, further comprising thestep of deciding which set of the plurality of parent sets becomes thefirst and second sets of the first pair.
 31. A method of fitting ahearing aid, comprising the steps: providing a hearing aid having aninitial prescription; preparing a population of a plurality of parentsets, each of the parent sets having at least one parent parameter;presenting a first pair of sets from the parent sets, the first paircomprising a first and second set and being presented with assistance ofthe hearing aid; selecting a first preference between the first andsecond sets of the first pair; presenting a second pair of sets from theparent sets, the second pair comprising a third and fourth set;selecting a second preference between the third and fourth sets of thesecond pair; operating on one set of the plurality of parent sets toobtain a child set, the child set being one of a mutation and crossoverof the one set, the child set having at least one child parameter;replacing one of the plurality of parent sets of the first populationwith the child set to form a second population; presenting a third pairof sets, the third pair comprising the child set and a fifth set, thefifth set being selected from the second population but not being thechild set; selecting a third preference between the child set and thefifth set; converging on a solution set, the solution set being one ofthe first, second, third, fourth, fifth and child sets; and updating theinitial prescription with one of the at least one parent and childparameters.
 32. A method of using a genetic algorithm in a system havinga first population of a plurality of parent sets, comprising the steps;presenting a first pair of sets from the parent sets, the first paircomprising a first and second set, the genetic algorithm selecting whichof the parent sets becomes the first and second set; indicating apreference to the genetic algorithm between the first and second sets ofthe first pair; operating on at least one set of the plurality of parentsets with a genetic algorithm operator to obtain a child set, the childset being one of a mutation and crossover; replacing one of theplurality of parent sets of the first population with the child set toform a second population; presenting a second pair, the second paircomprising the child set and a third set, the third set being selectedfrom the second population but not being the child set, the geneticalgorithm selecting which set of the second population becomes the thirdset; indicating a second preference to the genetic algorithm between thechild set and the third set of the second pair; and converging on asolution set, the solution set being one of the first, second, third andchild sets.
 33. A computer readable medium having executableinstructions for performing the steps of claim
 32. 34. A method ofperceptually tuning a system using a genetic algorithm, comprising thesteps: providing a system an initial setting; preparing a population ofa plurality of parent sets, each of the parent sets having at least oneparent parameter; presenting a first pair of sets from the parent sets,the first pair comprising a first and second set, the genetic algorithmselecting which of the plurality of parent sets becomes the first andsecond set; indicating a first preference to the genetic algorithmbetween the first and second sets of the first pair; presenting a secondpair of sets from the parent sets, the second pair comprising a thirdand fourth set, the genetic algorithm selecting which of the pluralityof parent sets becomes the third and fourth sets; indicating a secondpreference to the genetic algorithm between the third and fourth sets ofthe second pair; operating on one set of the plurality of parent setswith a genetic algorithm operator to obtain a child set, the child setbeing one of a mutation-and crossover of the one set, the child sethaving at least one child parameter; replacing one of the plurality ofparent sets of the first population with the child set to form a secondpopulation; presenting a third pair of sets, the third pair comprisingthe child set and a fifth set, the fifth set being selected from thesecond population but not being the child set, the genetic algorithmselecting which set of the second population becomes the fifth set;indicating a third preference to the genetic algorithm between the childset and the fifth set; converging on a solution set, the solution setbeing one of the first, second, third, fourth, fifth and child sets; andupdating the initial setting with one of the at least one parent andchild parameters selected from the solution set.
 35. A computer readablemedium having executable instructions for performing the steps of claim34.
 36. A method of using a genetic algorithm crossover operator on atleast a first and second parent set in a system population, each of theparent sets having a plurality of parent parameters, each the parentparameter being represented by a parent index wherein the parent indexescan be arranged in a range from a low the parent index to a high theparent index, comprising the steps: choosing a parent index position inthe first and second parent sets with some of the parent indexes in eachset of the first and second parent sets being to a left and right of theparent index position; creating a child set, the child set having achild index position, further comprising the steps, i) reproducing inthe child set to a left of the child index position, the parent indexesof one of the first and second parent sets to the left of the parentindex position; ii) reproducing in the child set to a right of the childindex position, the parent indexes of the other of the first and secondparent sets to the right of the parent index position; and iii) creatinga child index at the child index position that is any valuesubstantially equal to one of the low parent index, the high parentindex and any the parent index in the range.
 37. The method according toclaim 36, wherein the range is linear.
 38. A computer readable mediumhaving executable instructions for performing the steps of claim
 36. 39.A method of using a genetic algorithm mutation operator on at least oneparent set in a system population, the one parent set having a pluralityof parent parameters, each the parent parameter being represented by aparent index wherein the parent indexes can be arranged in a range froma low the parent index to a high the parent index, comprising the steps:creating a mutation set having a plurality of mutation indexes; summingthe mutation indexes and the parent indexes to form a summation sethaving a plurality of summation indexes; and reproducing the mutationindexes in a child set.
 40. The method according to claim 39, whereinthe one of the mutation indexes is one of larger than the high parentindex and smaller than the low parent index, wherein the step ofreproducing the mutation indexes in the child set further comprises thestep of rounding the one of the mutation indexes to one of the highparent index and the low parent index.
 41. The method according to claim39, wherein the step of creating the mutation set further comprisesgenerating the mutation indexes by generating a plurality of randomvariables and rounding each the random variable to a closest integer.42. The method according to claim 41, wherein the step of generating theplurality of random variables further comprises the step of creating anormal distribution for the random variables.
 43. A computer readablemedium having executable instructions for performing the steps of claim39.